List-Based OMP and an Enhanced Model for DOA Estimation With Nonuniform Arrays

• Published in: IEEE transactions on aerospace and electronic systems Vol. 57; no. 6; pp. 4457 - 4464
• Main Authors: Leite, Wesley S., Lamare, Rodrigo C. de
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.12.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Compressive sensing; Covariance matrices; Dictionaries; Direction of arrival; Direction-of-arrival estimation; Greedy algorithms; Indexing; Iterative methods; Linear arrays; Matched pursuit; Matching pursuit algorithms; Maximum likelihood estimation; nonuniform linear arrays; orthogonal matching pursuit; Sensor arrays; Signal classification; sparse recovery; Spatial smoothing
• ISSN: 0018-9251; 1557-9603
• DOI: 10.1109/TAES.2021.3087836

This article proposes an enhanced coarray transformation model (EDCTM) and a mixed greedy maximum likelihood algorithm called list-based maximum likelihood orthogonal matching pursuit (LBML-OMP) for direction-of-arrival estimation with nonuniform linear arrays (NLAs). The proposed EDCTM approach obtains improved estimates when Khatri-Rao product-based models are used to generate difference coarrays under the assumption of uncorrelated sources. In the proposed LBML-OMP technique, for each iteration a set of candidates is generated based on the correlation-maximization between the dictionary and the residue vector. LBML-OMP then chooses the best candidate based on a reduced-complexity asymptotic maximum likelihood decision rule. Simulations show the improved results of EDCTM over existing approaches and that LBML-OMP outperforms existing sparse recovery algorithms as well as spatial smoothing multiple signal classification with NLAs.